In recent years, the convolutional neural network (CNN) has greatly boosted the performance of many visual tasks. It is demonstrated to be rather effective for robust feature learning.
CNN-based models have been successfully applied in remote sensing image change detection [1]}. Wang et al. [2]} proposed an end-to-end CNN framework to learn discriminative features from mixed-affinity matrix for change detection. Later, the unsupervised deep noise modeling was developed for hyperspectral image change detection [3]}. Liu et al. [4]} proposed an elegant local restricted CNN (LR-CNN) for polarimetric SAR change detection. In [5]}, transferred deep learning was applied to sea ice SAR image change detection based on CNN. Although CNN-based methods have achieved excellent performance in change detection, the accuracy sometimes deteriorates under the case of transformation, such as tilts and rotations. Specifically, CNN is incapable of modeling the positional relationship among ground objects.
